A Method for Correcting Staggered Pulse Repetition Time (PRT) and Dual Pulse Repetition Frequency (PRF) Processor Errors in Research Radar Datasets

Abstract

Mobile weather radars at high frequencies (C, X, K, and W bands) often collect data using staggered pulse repetition time (PRT) or dual pulse repetition frequency (PRF) modes to extend the effective Nyquist velocity and mitigate velocity aliasing while maintaining a useful maximum unambiguous range. These processing modes produce widely dispersed “processor” dealiasing errors in radial velocity estimates. The errors can also occur in clusters in high shear areas. Removing these errors prior to quantitative analysis requires tedious manual editing and often produces “holes” or regions of missing data in high signal-to-noise areas. Here, data from three mobile weather radars were used to show that the staggered PRT errors are related to a summation of the two Nyquist velocities associated with each of the PRTs. Using observations taken during a mature mesoscale convective system, a landfalling tropical cyclone, and a tornadic supercell storm, an algorithm to automatically identify and correct staggered PRT processor errors has been developed and tested. The algorithm creates a smooth profile of Doppler velocities using a Savitzky–Golay filter independently in radius and azimuth and then combined. Errors are easily identified by comparing the velocity at each range gate to its smoothed counterpart and corrected based on specific error characteristics. The method improves past dual PRF correction methods that were less successful at correcting “grouped” errors. Given the success of the technique across low, moderate, and high radial shear regimes, the new method should improve research radar analyses by affording the ability to retain as much data as possible rather than manually or objectively removing erroneous velocities.